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Spectral Detection Technology, Sensors and Instruments, 2nd Edition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 13819

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Dr. Qing Yu

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Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Huaqiao University, Xiamen 361021, China
Interests: multispectral detection; confocal measurement
Special Issues, Collections and Topics in MDPI journals

Dr. Ran Tu

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Guest Editor

College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Interests: hazard detection and diagnosis; safety engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Ting Liu

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College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Interests: optical fiber sensing; spectral detection and micro-nano photonics
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Dr. Lina Li

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Guest Editor

College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Interests: spectral analysis technology; biomedical photoelectric testing instrument
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Special Issue Information

Dear Colleagues,

Spectral detection technology has always been a research hotspot in the field of precision measurement. It can not only obtain the spectral characteristics of the measured object, but also obtain the topography of samples through hyperspectral and multispectral. It is widely used in national defense, space remote sensing, biomedicine, disaster monitoring, and many engineering fields. In recent years, with the development of technology, intelligent manufacturing and environmental protection have attracted worldwide attention, which extends the application of spectral detection technology to new dimensions. Therefore, a Special Issue about "Spectral Detection Technology, Sensors and Instruments" is set up, in which we want to find innovative research work by scientists around the world in the following areas, but not limited to:

Multispectral detection technology;
Spectral analysis technology;
Spectral imaging processing;
Spectral-based hazard analysis and diagnosis;
Spectral-based environmental detecting and monitoring;
Medical detection based on spectrum;
Optical fiber spectral sensing technology.

Dr. Qing Yu
Dr. Ran Tu
Dr. Ting Liu
Dr. Lina Li
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

multispectral detection
spectral analysis
spectral imaging
spectral sensing

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Related Special Issue

Spectral Detection Technology, Sensors and Instruments in Sensors (14 articles)

Published Papers (11 papers)

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Research

20 pages, 5581 KiB

Open AccessArticle

Simulation Research and Analysis of Wavelength Modulation Off-Axis Integrated Cavity Output Spectrum Measurement System

by Tao Wu, Xiao Zhang, Xiao Chen, Wangwang Liu, Yan Han, Yubin Zhong, Dan Zhao, Zhen Fang, Linxin Pan, Feiyang Wang and Hang Xu

Sensors 2025, 25(8), 2478; https://doi.org/10.3390/s25082478 - 15 Apr 2025

Viewed by 212

Abstract

Wavelength modulation spectroscopy off-axis integrated cavity output spectroscopy (WMS-OA-ICOS) is an in situ detection technique suitable for analyzing trace gases in the atmospheres, characterized by its high sensitivity and ease of integration. However, in current practical applications, the design and optimization of WMS-OA-ICOS systems primarily rely on empirical knowledge, lacking systematic quantitative methodologies. To address this limitation, this study conducts comprehensive modeling and simulation research on WMS-OA-ICOS spectroscopy, proposing a novel modeling approach. The spot distribution simulation results obtained from the self-developed model are validated against those generated using Tracepro. Furthermore, based on the self-developed model, an in-depth investigation is conducted into the effects of cavity length tolerance, beam waist matching, modulation depth, and laser linewidth on signal quality. The findings provide valuable insights for designing and optimizing miniaturized systems with high signal-to-noise ratios. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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21 pages, 5289 KiB

Open AccessArticle

Research on the Transformer Failure Diagnosis Method Based on Fluorescence Spectroscopy Analysis and SBOA Optimized BPNN

by Xueqing Chen, Dacheng Li and Anjing Wang

Sensors 2025, 25(7), 2296; https://doi.org/10.3390/s25072296 - 4 Apr 2025

Viewed by 262

Abstract

The representative dissolved gases analysis (DGA) method for transformer fault detection faces many shortcomings in early fault diagnosis, which restricts the application and development of fault detection technology in the field of transformers. In order to diagnose early failure in time, fluorescence analysis technology has recently been used for the research of transformer failure diagnosis, which makes up for the shortcomings of DGA. However, most of the existing fluorescence analyses of insulating oil studies combined with intelligent algorithms are a qualitative diagnosis of fault types; the quantitative fault diagnosis of the same oil sample has not been reported. In this study, a typical fault simulation experiment of the interval discharge of insulating oil was carried out with the new Xinjiang Karamay oil, and the fluorescence spectroscopy data of insulating oil under different discharge durations were collected. In order to eliminate the influence of noise factors on the spectral analysis and boost the accuracy of the diagnosis, a variety of spectral preprocessing algorithms, such as Savitzky–Golay (SG), moving median, moving mean, gaussian, locally weighted linear regression smoothing (Lowess), locally weighted quadratic regression smoothing (Loess), and robust (RLowess) and (Rloess), are used to smooth denoise the collected spectral data. Then, the dimensionality reduction techniques of principal component analysis (PCA), kernel principal component analysis (KPCA), and multi-dimensional scale (MDS) are used for further processing. Based on various preprocessed and dimensionally reduced data, transformer failure diagnosis models based on the particle swarm optimization algorithm (PSO) and the secretary bird optimization algorithm (SBOA) optimized BPNN are established to quantitatively analyze the state of insulating oil and predict the durations of transformer failure. By using the mathematical evaluation methods to comprehensively evaluate and compare the effects of various algorithm models, it was found that the Loess-MDS-SBOA-BP model has the best performance, with its determination coefficient (R²) increasing to 99.711%, the root mean square error (RMSE) being only 0.27144, and the other evaluation indicators also being optimal. The experimental results show that the failure diagnosis model finally proposed in this paper can perform an accurate diagnosis of the failure time; the predicted time is closest to the true value, which lays a foundation for the further development of the field of transformer failure diagnosis. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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19 pages, 1098 KiB

Open AccessArticle

Deep Learning Based Pile-Up Correction Algorithm for Spectrometric Data Under High-Count-Rate Measurements

by Yiwei Huang, Xiaoying Zheng, Yongxin Zhu, Tom Trigano, Dima Bykhovsky and Zikang Chen

Sensors 2025, 25(5), 1464; https://doi.org/10.3390/s25051464 - 27 Feb 2025

Viewed by 377

Abstract

Gamma-ray spectroscopy is essential in nuclear science, enabling the identification of radioactive materials through energy spectrum analysis. However, high count rates lead to pile-up effects, resulting in spectral distortions that hinder accurate isotope identification and activity estimation. This phenomenon highlights the need for automated and precise approaches to pile-up correction. We propose a novel deep learning (DL) framework plugging count rate information of pile-up signals with a 2D attention U-Net for energy spectrum recovery. The input to the model is an Energy–Duration matrix constructed from preprocessed pulse signals. Temporal and spatial features are jointly extracted, with count rate information embedded to enhance robustness under high count rate conditions. Training data were generated using an open-source simulator based on a public gamma spectrum database. The model’s performance was evaluated using Kullback–Leibler (KL) divergence, Mean Squared Error (MSE) Energy Resolution (ER), and Full Width at Half Maximum (FWHM). Results indicate that the proposed framework effectively predicts accurate spectra, minimizing errors even under severe pile-up effects. This work provides a robust framework for addressing pile-up effects in gamma-ray spectroscopy, presenting a practical solution for automated, high-accuracy spectrum estimation. The integration of temporal and spatial learning techniques offers promising prospects for advancing high-activity nuclear analysis applications. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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22 pages, 4649 KiB

Open AccessArticle

Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging

by Zirak Khan, Seung-Chul Yoon and Suchendra M. Bhandarkar

Sensors 2025, 25(3), 970; https://doi.org/10.3390/s25030970 - 6 Feb 2025

Viewed by 767

Abstract

Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral imaging (HSI) offers a non-invasive mechanism to capture detailed spatial and spectral information, enabling the discrimination of different types of contaminants from poultry muscle and non-muscle external tissues. When integrated with advanced deep learning (DL) models, HSI systems can achieve high accuracy in detecting foreign materials. However, the high dimensionality of HSI data, the computational complexity of DL models, and the high-paced nature of poultry processing environments pose challenges for real-time implementation in industrial settings, where the speed of imaging and decision-making is critical. In this study, we address these challenges by optimizing DL inference for HSI-based foreign material detection through a combination of post-training quantization and hardware acceleration techniques. We leveraged hardware acceleration utilizing the TensorRT module for NVIDIA GPU to enhance inference speed. Additionally, we applied half-precision (called FP16) post-training quantization to reduce the precision of model parameters, decreasing memory usage and computational requirements without any loss in model accuracy. We conducted simulations using two hypothetical hyperspectral line-scan cameras to evaluate the feasibility of real-time detection in industrial conditions. The simulation results demonstrated that our optimized models could achieve inference times compatible with the line speeds of poultry processing lines between 140 and 250 birds per minute, indicating the potential for real-time deployment. Specifically, the proposed inference method, optimized through hardware acceleration and model compression, achieved reductions in inference time of up to five times compared to unoptimized, traditional GPU-based inference. In addition, it resulted in a 50% decrease in model size while maintaining high detection accuracy that was also comparable to the original model. Our findings suggest that the integration of post-training quantization and hardware acceleration is an effective strategy for overcoming the computational bottlenecks associated with DL inference on HSI data. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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17 pages, 11982 KiB

Open AccessArticle

Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning

by Zhonghua Liu, Jin Wang, Guorong Lyu, Haisheng Song, Weifeng Yu, Peizhong Liu, Yuling Fan and Yaocheng Wan

Sensors 2025, 25(3), 633; https://doi.org/10.3390/s25030633 - 22 Jan 2025

Viewed by 895

Abstract

Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model’s predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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29 pages, 8502 KiB

Open AccessArticle

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models

by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar and Noor Al Shakarji

Sensors 2025, 25(2), 303; https://doi.org/10.3390/s25020303 - 7 Jan 2025

Viewed by 987

Abstract

Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands. Sensitivity analyses were also conducted to identify the most sensitive bands for seed protein estimation. Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. Additionally, the CNN classification approach was used to estimate low, medium, and high protein concentrations because this type of classification is more applicable for breeding efforts. Our results showed that the proposed CNN with attention mechanisms predicted wheat protein content with R² values of 0.70 and 0.65 for ventral and dorsal seed orientations, respectively. Although, the R² of the CNN approach was lower than of the best performing feature-based method, RF (R² of 0.77), end-to-end prediction capabilities with CNN hold great promise for the automation of wheat protein estimation for breeding. The CNN model achieved better classification of protein concentrations between low, medium, and high protein contents, with an R² of 0.82. This study’s findings highlight the significant potential of hyperspectral imaging and machine learning techniques for advancing precision breeding practices, optimizing seed sorting processes, and enabling targeted agricultural input applications. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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22 pages, 9250 KiB

Open AccessArticle

High-Resolution Single-Pixel Imaging of Spatially Sparse Objects: Real-Time Imaging in the Near-Infrared and Visible Wavelength Ranges Enhanced with Iterative Processing or Deep Learning

by Rafał Stojek, Anna Pastuszczak, Piotr Wróbel, Magdalena Cwojdzińska, Kacper Sobczak and Rafał Kotyński

Sensors 2024, 24(24), 8139; https://doi.org/10.3390/s24248139 - 20 Dec 2024

Viewed by 967

Abstract

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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22 pages, 3388 KiB

Open AccessArticle

BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation

by Mohammad Rahman, Shyh Wei Teng, Manzur Murshed, Manoranjan Paul and David Brennan

Sensors 2024, 24(23), 7771; https://doi.org/10.3390/s24237771 - 4 Dec 2024

Viewed by 1108

Abstract

Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. Among these, attention-based algorithms have gained prominence by ranking bands based on their discriminative capability. However, they require a large number of model parameters, which increases the need for extensive training data. To address this challenge, we propose Band Selection through Discrete Relaxation (BSDR), a novel deep learning-based algorithm. BSDR reduces the number of learnable parameters by focusing solely on the target bands, which are typically far fewer than the original bands, thus resulting in a data-efficient configuration that minimizes training data requirements and reduces training time. The algorithm employs discrete relaxation, transforming the discrete problem of band selection into a continuous optimization task, which enables gradient-based search across the spectral dimension. Through extensive evaluations on three benchmark datasets with varying spectral dimensions and characteristics, BSDR demonstrates superior performance for both regression and classification tasks, achieving up to 25% and 34.6% improvements in overall accuracy, compared to the latest attention-based and traditional algorithms, respectively, while reducing execution time by 96.8% and 97.18%. These findings highlight BSDR’s effectiveness in addressing key challenges in hyperspectral band selection. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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22 pages, 2642 KiB

Open AccessArticle

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

by Renan Falcioni, Roney Berti de Oliveira, Marcelo Luiz Chicati, Werner Camargos Antunes, José Alexandre M. Demattê and Marcos Rafael Nanni

Sensors 2024, 24(19), 6490; https://doi.org/10.3390/s24196490 - 9 Oct 2024

Cited by 1 | Viewed by 1357

Abstract

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R² = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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23 pages, 10884 KiB

Open AccessArticle

Development of an Earth-Field Nuclear Magnetic Resonance Spectrometer: Paving the Way for AI-Enhanced Low-Field Nuclear Magnetic Resonance Technology

by Eduardo Viciana, Juan Antonio Martínez-Lao, Emilio López-Lao, Ignacio Fernández and Francisco Manuel Arrabal-Campos

Sensors 2024, 24(17), 5537; https://doi.org/10.3390/s24175537 - 27 Aug 2024

Cited by 1 | Viewed by 1507

Abstract

Today, it is difficult to have a high-field nuclear magnetic resonance (NMR) device due to the high cost of its acquisition and maintenance. These high-end machines require significant space and specialist personnel for operation and offer exceptional quality in the acquisition, processing, and other advanced functions associated with detected signals. However, alternative devices are low-field nuclear magnetic resonance devices. They benefit from the elimination of high-tech components that generate static magnetic fields and advanced instruments. Instead, they used magnetic fields induced by ordinary conductors. Another category of spectrometers uses the Earth’s magnetic field, which is simple and economical but limited in use. These devices are called Earth-Field Nuclear Magnetic Resonance (EFNMR) devices. This device is ideal for educational purposes, especially for engineers and those who study nuclear magnetic resonance, such as chemistry or other experimental sciences. Students can observe their internal workings and conduct experiments that complement their education without worrying about damaging equipment. This article provides a detailed explanation of the design and construction of electrical technology devices for the excitation of atomic spin resonance using Earth’s magnetic fields. It covers all necessary stages, from research to analysis, including simulation, assembly, construction of each component, and the development of comprehensive software for spectrometer control. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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16 pages, 1756 KiB

Open AccessArticle

Handheld Near-Infrared Spectroscopy for Undried Forage Quality Estimation

by William Yamada, Jerry Cherney, Debbie Cherney, Troy Runge and Matthew Digman

Sensors 2024, 24(16), 5136; https://doi.org/10.3390/s24165136 - 8 Aug 2024

Cited by 2 | Viewed by 4487

Abstract

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments—NeoSpectra, TrinamiX, and AgroCares—when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar

R^{2}

values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower

R^{2}

values. This study underscores the importance of understanding the water effects on undried forage, as water‘s high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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